RSS测距模式下水声传感器网络半正定松弛定位算法
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摘要
为了提高空间阴影相关环境下的未知节点的定位精度,考虑到水声环境的复杂性,提出了一种接收信号强度(RSS)测距模式下的水声传感器网络半正定松弛(SDR)定位算法。算法结合水声信道的传输损耗特性,建立了问题的优化函数和求解模型。同时,为了避免求解时出现最大似然收敛问题,通过松弛约束将非凸优化问题等价转化为凸问题,并运用凸优化理论中的SDR算法求解未知节点的位置。在空间阴影不相关和相关两种情况下,提出算法分别与最大似然定位算法和最小二乘算法进行了比较。仿真分析表明:提出算法在不相关和相关的阴影环境中都具有较高的估计精度。
In order to improve positioning precision of unknown nodes in space shadow-related environment,due to the complexity of underwater acoustic environment,a semidefinite relaxation( SDR) localization algorithm for underwater acoustic sensor networks is proposed under received signal strength( RSS) ranging mode. Combined with transmission loss characteristics of the underwater acoustic channel,the optimization function and the solution model for the problem are established. At the same time,in order to avoid the maximum likelihood convergence problem occur in resolving,the nonconvex optimization problem is transformed into convex problem by relaxation constraint,and unknown nodes location is solved by SDR algorithm in convex optimization theory. In the cases of both irrelevant and relevant spatial shadow,the proposed algorithm is compared with the maximum likelihood localization algorithm and the least squares algorithm respectively. The simulation analysis show that the proposed algorithm has high estimation precision in irrelevant and relevant shadow environments.
In order to improve positioning precision of unknown nodes in space shadow-related environment,due to the complexity of underwater acoustic environment,a semidefinite relaxation( SDR) localization algorithm for underwater acoustic sensor networks is proposed under received signal strength( RSS) ranging mode. Combined with transmission loss characteristics of the underwater acoustic channel,the optimization function and the solution model for the problem are established. At the same time,in order to avoid the maximum likelihood convergence problem occur in resolving,the nonconvex optimization problem is transformed into convex problem by relaxation constraint,and unknown nodes location is solved by SDR algorithm in convex optimization theory. In the cases of both irrelevant and relevant spatial shadow,the proposed algorithm is compared with the maximum likelihood localization algorithm and the least squares algorithm respectively. The simulation analysis show that the proposed algorithm has high estimation precision in irrelevant and relevant shadow environments.
引文
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[2] Erolkantarci M,Mouftah H T,Oktug S. A survey of architectures and localization techniques for underwater acoustic sensor networks[J]. IEEE Communications Surveys&Tutorials,2011,13(3):487-502.
[3] Peng J. An underwater sensor network localization algorithm based on time-of-arrivals(TOAs)[C]∥The Ninth International Conference on Natural Computation,IEEE,2013:1552-1556.
[4] Emokpae L E,Dibenedetto S,Potteiger B,et al. UREAL:Underwater reflection-enabled acoustic-based localization[J]. Sensors Journal IEEE,2014,14(11):3915-3925.
[5] Hosseini M,Chizari H,Ismail,A. S. New hybrid RSS-based localization mechanism for underwater wireless sensor networks[J].International Journal of Computer Communications and Networks,2011,1(1):1-10.
[6] Hosseini M,Chizari H,Chai K S,et al. RSS-based distance measurement in underwater acoustic sensor networks:An application of the Lambert W function[C]∥International Conference on Signal Processing and Communication Systems,IEEE,2011:1-4.
[7] Kim C,Lee S,Kim K. 3D underwater localization with hybrid ranging method for near-sea marine monitoring[C]∥International Conference on Embedded and Ubiquitous Computing,IEEE,2011:438-441.
[8] Yan Y S,Wang H Y,Shen X H,et al. Efficient convex optimization method for underwater passive source localization based on RSS with WSNs[C]∥IEEE International Conference on Signal Processing,Communication and Computing,IEEE,2012:171-174.
[9] Lee J H,Buehrer R M. Location estimation using differential RSS with spatially correlated shadowing[C]∥2009 Global Telecommunications Conference,GLOBECOM 2009,IEEE,2009:1-6.